COMPLETE ROADMAP TO MASTER
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Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
STEP-1: Programming Language[Python / R]
- Variables
- Mathematical Operators
- Control Statements
- Data Structures (List, Set, Dict, etc.)
- Work with files
- Functions
- Object-Oriented Programming
STEP-2: Linear Algebra
STEP-3: Statistics
STEP-4: Core Machine Learning Algorithms
As a machine learning engineer, you should be a master of the following concept:
- Clean Data
- Fill Missing Value
- Drop Some Feature
- Feature Selection
- Feature Scaling
- Regularization
- Feature Engineering (optional at first)
- Regression Algorithms
- Simple Linear Regression
- Ridge & Lasso
- Multiple Linear Regression
- Polynomial Regression
- XGBRegressor
- Classification Algorithms
- KNN (K Nearest Neighbor)
- Logistic Regression
- Decision Tree
- Random Forest
- Naive Bayes
- XGBClassifier
- Clustering Algorithms
- K-Means
- DBSCAN (Density-based spatial clustering of applications)
- Dimensionality Reduction
- PCA (Principale Component Analysis)
- LDA
- t-SNE
Machine Learning in Practice
You should learn sklearn to implement all last step concepts. There are also many courses in this library. You can also use sklearn documentation.
Resources
An amazing resource to learn about all this is a book called “Hands-on ML with Scikit learn and TensorFlow.” (Not an affiliate link)
Try to grab a copy of this book. It will help you a lot.
There are a few other resources too that are worth looking into: